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This paper demonstrates for the first time that a biologically-plausiblespiking neural network (SNN) equipped with Spike-Timing-Dependent Plasticity(STDP) can continuously learn to detect walking people on the fly usingretina-inspired, event-based cameras. Our pipeline works as follows. First, ashort sequence of event data ($<2$ minutes), capturing a walking human by aflying drone, is forwarded to a convolutional SNNSTDP system which alsoreceives teacher spiking signals from a readout (forming a semi-supervisedsystem). Then, STDP adaptation is stopped and the learned system is assessed ontesting sequences. We conduct several experiments to study the effect of keyparameters in our system and to compare it against conventionally-trained CNNs.We show that our system reaches a higher peak $F_1$ score (+19%) compared toCNNs with event-based camera frames, while enabling on-line adaptation.

Continuously Learning to Detect People on the Fly: A Bio-inspired Visual System for Drones